


'''simple docstring'''






import numpy

# List of input, output pairs
__UpperCAmelCase       							=				(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
__UpperCAmelCase       							=				(((515, 22, 13), 555), ((61, 35, 49), 150))
__UpperCAmelCase       							=				[2, 4, 1, 5]
__UpperCAmelCase       							=				len(train_data)
__UpperCAmelCase       							=				0.009


def    _snake_case  (	A					,       A="train"			)		->       Optional[Any]:
							return calculate_hypothesis_value(A					,       A			) - output(
							    A					,       A			)


def    _snake_case  (	A			)		->       int:
							lowerCAmelCase__			  =      0
							for i in range(len(A			) - 1			):
														hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
							hyp_val += parameter_vector[0]
							return hyp_val


def    _snake_case  (	A					,       A			)		->       List[Any]:
							if data_set == "train":
														return train_data[example_no][1]
							elif data_set == "test":
														return test_data[example_no][1]
							return None


def    _snake_case  (	A					,       A			)		->       int:
							if data_set == "train":
														return _hypothesis_value(train_data[example_no][0]			)
							elif data_set == "test":
														return _hypothesis_value(test_data[example_no][0]			)
							return None


def    _snake_case  (	A					,       A=m			)		->       Tuple:
							lowerCAmelCase__			  =      0
							for i in range(A			):
														if index == -1:
																					summation_value += _error(A			)
														else:
																					summation_value += _error(A			) * train_data[i][0][index]
							return summation_value


def    _snake_case  (	A			)		->       List[str]:
							lowerCAmelCase__			  =      summation_of_cost_derivative(A					,       A			) / m
							return cost_derivative_value


def    _snake_case  (	)		->       str:
							global parameter_vector
							# Tune these values to set a tolerance value for predicted output
							lowerCAmelCase__			  =      0.000_002
							lowerCAmelCase__			  =      0
							lowerCAmelCase__			  =      0
							while True:
														j += 1
														lowerCAmelCase__			  =      [0, 0, 0, 0]
														for i in range(0					,       len(A			)			):
																					lowerCAmelCase__			  =      get_cost_derivative(i - 1			)
																					lowerCAmelCase__			  =      (
																					    parameter_vector[i] - LEARNING_RATE * cost_derivative
																					)
														if numpy.allclose(
														    A					,       A					,       atol=A					,       rtol=A					,       ):
																					break
														lowerCAmelCase__			  =      temp_parameter_vector
							print(('''Number of iterations:''', j)			)


def    _snake_case  (	)		->       Union[str, Any]:
							for i in range(len(A			)			):
														print(('''Actual output value:''', output(A					,       '''test'''			))			)
														print(('''Hypothesis output:''', calculate_hypothesis_value(A					,       '''test'''			))			)


if __name__ == "__main__":
		run_gradient_descent()
		print('''\nTesting gradient descent for a linear hypothesis function.\n''')
		test_gradient_descent()







'''simple docstring'''





import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional

import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm

import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy


_UpperCamelCase						:						List[str]  =					logging.getLogger(__name__)

_UpperCamelCase						:						int  =					'pytorch_model.bin'


@dataclasses.dataclass
class  snake_case__      :
					a_        =					dataclasses.field(
					    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models."})
					a_        =					dataclasses.field(
					    default=UpperCamelCase     ,				metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co."}     ,				)






@dataclasses.dataclass
class  snake_case__      :
					a_        =					dataclasses.field(metadata={"help": "A csv or a json file containing the training data."})
					a_        =					dataclasses.field(metadata={"help": "A csv or a json file containing the data to predict on."})
					a_        =					dataclasses.field(
					    default=UpperCamelCase     ,				metadata={"help": "A csv or a json file containing the validation data."})
					a_        =					dataclasses.field(
					    default=UpperCamelCase     ,				metadata={"help": "The name of the task to train on."}     ,				)
					a_        =					dataclasses.field(
					    default=UpperCamelCase     ,				metadata={"help": "The list of labels for the task."})






@dataclasses.dataclass
class  snake_case__      :
					a_        =					dataclasses.field(
					    metadata={"help": "The output directory where the model predictions and checkpoints will be written."})
					a_        =					dataclasses.field(
					    default="accuracy"     ,				metadata={"help": "The evaluation metric used for the task."})
					a_        =					dataclasses.field(
					    default="no"     ,				metadata={
					        "help": "The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]"
					    }     ,				)
					a_        =					dataclasses.field(
					    default=10     ,				metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."}     ,				)
					a_        =					dataclasses.field(
					    default=0.0     ,				metadata={
					        "help": "How much the specified evaluation metric must improve to satisfy early stopping conditions."
					    }     ,				)
					a_        =					dataclasses.field(
					    default=UpperCamelCase     ,				metadata={"help": "Whether to filter the pseudo-labeled data based on the confidence score."}     ,				)
					a_        =					dataclasses.field(
					    default=UpperCamelCase     ,				metadata={"help": "Whether to filter the pseudo-labeled data based on the validation performance."}     ,				)
					a_        =					dataclasses.field(
					    default=UpperCamelCase     ,				metadata={"help": "Whether to fine-tune on labeled data after pseudo training."}     ,				)
					a_        =					dataclasses.field(
					    default=0.0     ,				metadata={"help": "Confidence threshold for pseudo-labeled data filtering."}     ,				)
					a_        =					dataclasses.field(
					    default=100     ,				metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."}     ,				)
					a_        =					dataclasses.field(
					    default=UpperCamelCase     ,				metadata={"help": "Random seed for initialization."}     ,				)






def __UpperCAmelCase   (					A					:			str			,       A					:			Optional[Any]			,       A					:			List[Any]			,       A					:			Any			,       A					:			Union[str, Any]			,       A					:			Dict		)    ->   int:
			UpperCAmelCase_      :       Tuple          =       datasets.concatenate_datasets([infer_input, infer_output]			,       axis=1		)

			if args.do_filter_by_confidence:
						UpperCAmelCase_      :       Tuple          =       dataset.filter(lambda A		: example["probability"] > args.confidence_threshold		)

			if args.do_filter_by_val_performance:
						assert eval_result >= 0.0 and eval_result <= 1.0
						UpperCAmelCase_      :       Dict          =       int(eval_result * len(A		)		)
						print(A		)
						UpperCAmelCase_      :       Optional[int]          =       dataset.sort('''probability'''			,       reverse=A		)
						UpperCAmelCase_      :       Optional[Any]          =       dataset.select(range(A		)		)

			UpperCAmelCase_      :       Dict          =       dataset.remove_columns(['''label''', '''probability''']		)
			UpperCAmelCase_      :       List[Any]          =       dataset.rename_column('''prediction'''			,       '''label'''		)
			UpperCAmelCase_      :       List[str]          =       dataset.map(lambda A		: {"label": idalabel[example["label"]]}		)
			UpperCAmelCase_      :       Union[str, Any]          =       dataset.shuffle(seed=args.seed		)

			UpperCAmelCase_      :       str          =       os.path.join(A			,       F"train_pseudo.{args.data_file_extension}"		)
			if args.data_file_extension == "csv":
						dataset.to_csv(A			,       index=A		)
			else:
						dataset.to_json(A		)







def __UpperCAmelCase   (					A					:			Any			,       A					:			int			,       A					:			Union[str, Any]			,       A					:			Dict			,       **A					:			Any		)    ->   Dict:
			UpperCAmelCase_      :       List[str]          =       Accelerator()
			# Make one log on every process with the configuration for debugging.
			logging.basicConfig(
			    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''			,       datefmt='''%m/%d/%Y %H:%M:%S'''			,       level=logging.INFO			,       )
			logger.info(accelerator.state		)

			# Setup logging, we only want one process per machine to log things on the
			# screen. accelerator.is_local_main_process is only True for one process per
			# machine.
			logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR		)

			if accelerator.is_local_main_process:
						datasets.utils.logging.set_verbosity_warning()
						transformers.utils.logging.set_verbosity_info()
			else:
						datasets.utils.logging.set_verbosity_error()
						transformers.utils.logging.set_verbosity_error()

			UpperCAmelCase_      :       Tuple          =       STModelArguments(model_name_or_path=A		)
			UpperCAmelCase_      :       int          =       STDataArguments(train_file=A			,       infer_file=A		)
			UpperCAmelCase_      :       Optional[Any]          =       STTrainingArguments(output_dir=A		)
			UpperCAmelCase_      :       Optional[int]          =       argparse.Namespace()

			for arg_class in (model_args, data_args, training_args):
						for key, value in vars(A		).items():
									setattr(A			,       A			,       A		)

			for key, value in kwargs.items():
						if hasattr(A			,       A		):
									setattr(A			,       A			,       A		)

    # Sanity checks
			UpperCAmelCase_      :       List[str]          =       {}
			UpperCAmelCase_      :       Any          =       None

			# You need to provide the training data and the data to predict on
			assert args.train_file is not None
			assert args.infer_file is not None
			UpperCAmelCase_      :       List[Any]          =       args.train_file
			UpperCAmelCase_      :       Optional[int]          =       args.infer_file

			if args.evaluation_strategy != IntervalStrategy.NO.value:
						assert args.eval_file is not None
						UpperCAmelCase_      :       List[str]          =       args.eval_file

			for key in data_files:
						UpperCAmelCase_      :       Dict          =       data_files[key].split('''.'''		)[-1]
						assert extension in ["csv", "json"], F"`{key}_file` should be a csv or a json file."
						if args.data_file_extension is None:
									UpperCAmelCase_      :       Any          =       extension
						else:
									assert extension == args.data_file_extension, F"`{key}_file` should be a {args.data_file_extension} file`."

			assert (
			    args.eval_metric in datasets.list_metrics()
			), F"{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}."

			# If passed along, set the training seed now.
			if args.seed is not None:
						set_seed(args.seed		)

			logger.info('''Creating the initial data directory for self-training...'''		)
			UpperCAmelCase_      :       Any          =       F"{args.output_dir}/self-train_iter-{{}}".format
			UpperCAmelCase_      :       Any          =       data_dir_format(0		)

			if accelerator.is_main_process:
						if args.output_dir is not None:
									os.makedirs(args.output_dir			,       exist_ok=A		)
									os.makedirs(A			,       exist_ok=A		)
			accelerator.wait_for_everyone()

			UpperCAmelCase_      :       Union[str, Any]          =       None
			UpperCAmelCase_      :       Tuple          =       None
			UpperCAmelCase_      :       Optional[Any]          =       0
			UpperCAmelCase_      :       int          =       False
			# Show the progress bar
			UpperCAmelCase_      :       int          =       tqdm(range(args.max_selftrain_iterations		)			,       disable=not accelerator.is_local_main_process		)

			# Self-train
			for iteration in range(0			,       int(args.max_selftrain_iterations		)		):
						UpperCAmelCase_      :       str          =       data_dir_format(A		)
						assert os.path.exists(A		)

						# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
						# iteration > 0
						UpperCAmelCase_      :       List[Any]          =       os.path.join(A			,       '''stage-1'''		)
						UpperCAmelCase_      :       Dict          =       {
						    '''accelerator''': accelerator,
						    '''model_name_or_path''': args.model_name_or_path,
						    '''cache_dir''': args.cache_dir,
						    '''do_train''': True,
						    '''train_file''': data_files['''train'''] if iteration == 0 else data_files['''train_pseudo'''],
						    '''do_eval''': True if args.eval_file is not None else False,
						    '''eval_file''': data_files['''eval'''],
						    '''do_predict''': True,
						    '''infer_file''': data_files['''infer'''],
						    '''task_name''': args.task_name,
						    '''label_list''': args.label_list,
						    '''output_dir''': current_output_dir,
						    '''eval_metric''': args.eval_metric,
						    '''evaluation_strategy''': args.evaluation_strategy,
						    '''early_stopping_patience''': args.early_stopping_patience,
						    '''early_stopping_threshold''': args.early_stopping_threshold,
						    '''seed''': args.seed,
						}
						# Add additional training arguments
						for key, value in kwargs.items():
									if key not in arguments_dict and not hasattr(A			,       A		):
												arguments_dict.update({key: value}		)

						UpperCAmelCase_      :       int          =       os.path.join(A			,       '''best-checkpoint'''			,       A		)
						if os.path.exists(A		):
									logger.info(
									    '''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.'''			,       A			,       A			,       )
						else:
									logger.info('''***** Running self-training: iteration: %d, stage: 1 *****'''			,       A		)
									finetune(**A		)
									accelerator.wait_for_everyone()
									assert os.path.exists(A		)
									logger.info('''Self-training job completed: iteration: %d, stage: 1.'''			,       A		)

						if iteration > 0 and args.finetune_on_labeled_data:
									# Stage 2 (optional): fine-tuning on the original labeled data
									UpperCAmelCase_      :       Any          =       os.path.join(A			,       '''best-checkpoint'''		)
									UpperCAmelCase_      :       Optional[int]          =       os.path.join(A			,       '''stage-2'''		)
									# Update arguments_dict
									UpperCAmelCase_      :       List[Any]          =       model_path
									UpperCAmelCase_      :       Optional[int]          =       data_files['''train''']
									UpperCAmelCase_      :       Union[str, Any]          =       current_output_dir

									UpperCAmelCase_      :       Optional[int]          =       os.path.join(A			,       '''best-checkpoint'''			,       A		)
									if os.path.exists(A		):
												logger.info(
												    '''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.'''			,       A			,       A			,       )
									else:
												logger.info('''***** Running self-training: iteration: %d, stage: 2 *****'''			,       A		)
												finetune(**A		)
												accelerator.wait_for_everyone()
												assert os.path.exists(A		)
												logger.info('''Self-training job completed: iteration: %d, stage: 2.'''			,       A		)

						UpperCAmelCase_      :       Dict          =       iteration
						UpperCAmelCase_      :       Optional[Any]          =       data_dir_format(iteration + 1		)

						UpperCAmelCase_      :       Any          =       AutoConfig.from_pretrained(os.path.join(A			,       '''best-checkpoint'''		)		)
						UpperCAmelCase_      :       Optional[Any]          =       config.idalabel
						UpperCAmelCase_      :       List[Any]          =       os.path.join(A			,       '''eval_results_best-checkpoint.json'''		)
						UpperCAmelCase_      :       Union[str, Any]          =       os.path.join(A			,       '''test_results_best-checkpoint.json'''		)
						assert os.path.exists(A		)

						with open(A			,       '''r'''		) as f:
									UpperCAmelCase_      :       Optional[int]          =       float(json.load(A		)[args.eval_metric]		)
						UpperCAmelCase_      :       List[Any]          =       os.path.join(A			,       '''infer_output_best-checkpoint.csv'''		)
						assert os.path.exists(A		)
						# Loading the dataset from local csv or json files.
						UpperCAmelCase_      :       str          =       load_dataset(args.data_file_extension			,       data_files={'''data''': data_files['''infer''']}		)['''data''']
						UpperCAmelCase_      :       Union[str, Any]          =       load_dataset('''csv'''			,       data_files={'''data''': infer_output_file}		)['''data''']

						if accelerator.is_main_process:
									os.makedirs(A			,       exist_ok=A		)
									shutil.copy(A			,       os.path.join(A			,       F"eval_results_iter-{iteration}.json"		)		)
									if os.path.exists(A		):
												shutil.copy(A			,       os.path.join(A			,       F"test_results_iter-{iteration}.json"		)		)
									create_pseudo_labeled_data(A			,       A			,       A			,       A			,       A			,       A		)
						accelerator.wait_for_everyone()

						UpperCAmelCase_      :       int          =       os.path.join(A			,       F"train_pseudo.{args.data_file_extension}"		)

						if args.evaluation_strategy != IntervalStrategy.NO.value:
									UpperCAmelCase_      :       List[str]          =       eval_result

									if best_iteration is None:
												UpperCAmelCase_      :       Any          =       new_iteration
												UpperCAmelCase_      :       List[Any]          =       new_eval_result
									else:
												if new_eval_result - best_eval_result > args.early_stopping_threshold:
															UpperCAmelCase_      :       str          =       new_iteration
															UpperCAmelCase_      :       Optional[int]          =       new_eval_result
															UpperCAmelCase_      :       Any          =       0
												else:
															if new_eval_result == best_eval_result:
																		UpperCAmelCase_      :       int          =       new_iteration
																		UpperCAmelCase_      :       List[Any]          =       new_eval_result
															early_stopping_patience_counter += 1

												if early_stopping_patience_counter >= args.early_stopping_patience:
															UpperCAmelCase_      :       List[Any]          =       True

						progress_bar.update(1		)

						if should_training_stop:
									break

			if best_iteration is not None:
						# Save the best iteration
						logger.info('''Best iteration: %d'''			,       A		)
						logger.info('''Best evaluation result: %s = %f'''			,       args.eval_metric			,       A		)
						accelerator.wait_for_everyone()
						if accelerator.is_main_process:
									shutil.copy(
									    os.path.join(A			,       F"eval_results_iter-{iteration}.json"		)			,       os.path.join(A			,       '''eval_results_best-iteration.json'''		)			,       )
			else:
						# Assume that the last iteration is the best
						logger.info('''Best iteration: %d'''			,       args.max_selftrain_iterations - 1		)
						logger.info('''Best evaluation result: %s = %f'''			,       args.eval_metric			,       A		)
						accelerator.wait_for_everyone()
						if accelerator.is_main_process:
									shutil.copy(
									    os.path.join(A			,       F"eval_results_iter-{args.max_selftrain_iterations - 1}.json"		)			,       os.path.join(A			,       '''eval_results_best-iteration.json'''		)			,       )


